DE Seminar: Caleb Phillips, Mac Luu, and Matthew Lastner
Undergraduate Students
Title: Parameter Estimation and Sensitivity Applied to a Novel Malaria Outbreak in Dire Dawa, Ethiopia
Speaker: Mac Luu
Abstract: Malaria, a devastating vector-borne disease, poses a significant global public health threat. The emergence of the exotic Anopheles stephensi presents a new urban vector of malaria and raises concerns about the dynamic spread of the disease in the traditionally non-malarial Dire Dawa region of Ethiopia. We consider a mechanistic differential equation Susceptible-Exposed-Infected-Recovered (SEIR) model for human malarial transmission where the primary parameters of interest are the rate at which individuals are exposed to malarial mosquitoes $\beta$ and the rate at which exposed individuals become infected $\alpha _1$. Our goal is to use a novel dataset that includes the number of individuals exposed to and infected with malaria in the Dire Dawa region to improve model predictions. In particular, we use Sobol sequence sensitivity techniques to identify the sensitive parameters of the model and subsequently use Bayesian inference and data assimilation techniques to estimate parameters from the novel dataset in the SEIR model. Given the inherent uncertainties and variability in real-world epidemiological data, Bayesian inference and nudging arise as a powerful tool for parameter estimation. Using Bayesian inference, we update prior knowledge on the transmission parameters based on the observed data, thereby refining the estimates of $\beta$ and $\alpha_1$ with derived posterior distributions. Concurrently, nudging was employed to iteratively adjust the SEIR model's state towards the observed state, enhancing the model's fidelity to real-world data. These methods ensure that the model remains closely aligned with the observed outbreak dynamics, improving the reliability of predictions and providing a framework for parameter estimation models where parameters can not be observed.